A Knowledge Aided Space Time Adaptive Processing Based on Road Network Data

The echo of the vehicle from the main lobe may contaminate the training samples of Space Time Adaptive Processing (STAP), which results in target self nulling effect, and therefore degrades the probability of detection. To mitigate this problem, this paper proposes a Knowledge Aided (KA) STAP which is based on the road network data to select the training samples. This study firstly estimates the radial velocity of vehicle to the radar; then the range-Doppler cells which may contain vehicle echo are obtained according to the velocity; in the following, this study distinguish whether the training samples contain vehicle echo according to the matching degree of the training samples with the steering vector of the main lobe and the clutter; finally, the samples containing vehicle echo are discarded when the covariance matrix for the STAP is estimated. The theory analysis and experimental results illustrate that the proposed method advances the output of signal to clutter plus noise ratio, and improves the performance of STAP in the road network environments.